Systems and methods to facilitate analytics with a tagged corpus

ABSTRACT

The disclosed embodiments provide a set of methods, systems, data structures, and computer-executable instructions for executing on a compute machine to automatically analyze data associated with an indexed corpora and to generate for graphical display a set of results associated with those analytic operations.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.13/655,841, entitled “System and Methods to Facilitate Analytics with aTagged Corpus”, and filed on Oct. 19, 2012.

BACKGROUND

Field

The present specification generally relates to computerized analytics.

Technical Background

Globalization, lean operations, outsourcing, supply base complexity, allincrease use of outsourcing and unprecedented number of supply chaindisruptions. The average cost is estimated between $10M-$50M/year withcorresponding damage to future stock returns. Supplier behavior can alsodamage an organization's corporate brand and shareholder value.Additionally, new laws (e.g., California Supply Chain Transparency Act)require organizations to publicly post how they maintain supply chainvisibility. Manually monitoring all the Suppliers (which can number inthe thousands for the average Fortune 1000 organization) and theirproblems is an impossible task. Quarterly financial scores, from anentity such as Dun & Bradstreet, are unlikely to be helpful in such ascenario as well since there wasn't a known financial risk to track.Also, this approach is not sufficient to catch important events in timeto mitigate or avoid threats to their supply chain—by the time aquarterly report has run, the damage may be done. Additionally, entitiesmay have Suppliers with multiple manufacturing facilities around theglobe. For instance, when a tsunami hits an industry-heavy location, acustomer may spend days calling Suppliers to find out who was impactedbecause they have no way of matching Suppliers to locations.

Current monitoring tools have significant deficiencies including aninability to automatically coordinate events, Suppliers, locations andindustries across a corpus of content as well as evaluate/score andsurface that content. Therefore, a need exists to collect and analyzedata to highlight potential risk scenarios in advance of a situation toavoid possible negative outcomes such as compliance/SOX/monetaryfailures/losses. These risks may come from unreliable/bad Suppliers(e.g., financial distress, product recalls, ethical issues or simplymacro location or industry-based events, such as the Japan tsunami thatcreated massive supply chain disruptions in the computer and autoindustries).

In other embodiments, there is a need for a monitoring tool to act as azeitgeist tracker particularly one that can measure awareness of issuesin a specific geographic region as well as across multiple regions oreven globally for comparison purposes. Prior art systems and methodsmight measure the frequency of word terms in a sample set but theresults from such analytics are unreliable because a long article, usinga keyword with a high frequency, can skew the result. Thus, a system tomeasure the prevalence of topics based on the number of articles inwhich certain concepts appear is needed.

Thus, there is a need in the industry for a comprehensive monitoringtool. There is a further need for such a tool focused analysis of supplychain management. Monitoring can come in many forms including focusedand comparative consciousness/zeitgeist tracking across geographicregions. Monitoring may also be used to identify areas of opportunityfor sales or alternative vendor/Supplier relationships.

SUMMARY

Embodiments of the systems comprise multiple levels of functionality aswell as varying depth and breadth in the graphical user interfacesgenerated by such embodiments.

In one embodiment, a system may be configured to perform analytics tofacilitate issue awareness comprising at least one computer-readablestorage medium on which a database management system is stored andconfigured to access an index of metadata corresponding to content itemsin a corpora of electronically stored content. It may also include atleast one sub-system configured to generate at least one interactivegraphical user interface (GUI) for display on a computer-based visualsub-system. It may also include at least one sub-system configured toreceive a query request configured by a user in said interactive GUIwherein said query request includes a user-defined threshold. It mayalso include a computer machine configured to receive said query ascomputer machine input; search a set of metadata tags stored in saidindex for at least one term contained in said query; identify a set ofmetadata tags which match said query; identify at least one document, insaid corpora, which is associated with said set of metadata tags;calculate a content score for each document identified in the previousstep; if said content score exceeds said user-defined threshold, surfacesaid document; calculate a summary score for a set of documents surfacedin the previous step based on the content scores associated with saiddocuments; generate for graphical display a second interactive userinterface to communicate said summary score wherein said second GUI isconfigured to permit a user to click through said summary score.

In another embodiment, a user may be permitted to click through saidsummary score to reveal a set of documents from which said summary scorewas derived. Alternatively, a system may reveal a list of documenttitles representing a set of documents from which said summary score wasderived with click through functionality for each title in said list todisplay a document associated with said title.

A system may include a database for storing a metadata index and acorpora of documents and a GUI. Such a system may be further configuredto search a metadata index for tags to identify a set of documents,calculate various scores and summaries and generate a graphical displayof those results that may be clicked through to reveal the underlyingdata which developed those scores.

In another embodiment, the query may include at least one entityprofile, a method to calculate a summary score by averaging certainunderlying document scores which may be displayed in a grid. A systemmay be further configured to include at least one entity in said queryrequest. It may further average said content scores for said set ofsurfaced documents for each of said at least one entity to develop asummary score. Then it may display each summary score in a grid via thesecond GUI.

In another embodiment, an entity profile may comprise a set of tags aswell as other score calculations such as tier scores and suppliercategory scores. A system may be configured so that an entity profileassociated with said entity comprises a set of tags, a supplier tier, asupplier tier weight, a supplier category, and a supplier categoryweight and wherein said set of tags in said entity profile are includedin said query. The query request may comprise a risk category weight.Then the system may be further configured to calculate a tier score byaveraging said summary scores for all entities assigned to a given tier;a weighted tier score for said tier by applying said tier weight; and asupplier category score by selecting a maximum score associated with asupplier within a given supplier category and applying a suppliercategory weight.

In another embodiment, a query request comprises at least one entityprofile and at least one risk category and risk category weight. Thesummary score averages said content scores for said set of surfaceddocuments for each of said at least one entity profile in each of saidat least one risk category; and said second GUI displays each summaryscore in a grid juxtaposing a set of suppliers against a set of riskcategories.

In another embodiment, risk categories may be chosen from the listconsisting of environmental issues, economic issues, societal issues,political issues, technology issues, business-specific issues and legalissues.

In another embodiment, the second GUI is further configured to expandsaid set of risk categories into a set of risk dimensions comprising acompany perspective, an industry perspective, and a locationperspective. The summary score for the company perspective may be basedon a subset of said surfaced documents comprising a match with at leastone company name associated with said entity profile. The summary scorefor said industry perspective may be based on a subset of said surfaceddocuments comprising a match with at least one industry tag associatedwith said entity profile. The summary score for said locationperspective may be based on a subset of said surfaced documentscomprising a match with at least one location tag associated with saidentity profile.

In another embodiment, an administrative subsystem may be configured togenerate for graphical display on a computer-based visual sub-system aninteractive administrative GUI to allow a user to configure at least oneentity profile. An administration subsystem may comprise a computermachine configured to Generate for Graphical Display and performprocessing associated with setting up the system prior to use by anend-user. The entity profile may include a supplier and a set of tagsassociated with said supplier including a supplier tier, a supplier tierweight, a supplier category, and a supplier category weight. Theadministrative subsystem may then receive and store said entity profilein a computer-readable storage medium.

In another embodiment, a system may be configured so that the queryrequest comprises at least two time periods and at least one geographicdesignation. The query request further includes a subject chosen from aset of subjects contained in said metadata index. The summary scorecounts said set of surfaced documents for each of said at least two timeperiods. The second GUI then graphically compares said summary scoreassociated with each of said at least two time periods.

In another embodiment, the query request may be further configured sothat the at least two time periods comprise a baseline time period and asecond time period and the threshold comprises a minimum relevancelevel.

In another embodiment, the index of metadata comprises a set ofgeographic tags and wherein each content item in said corpora ofelectronically stored content is associated with a tag corresponding tosaid content item's region of publication.

In another embodiment, the second GUI graphically displays said summaryscores for each of said geographic designations.

In another embodiment the query request may be further configured byproviding a set of weights to use as computer machine input to determineif a document meets or exceeds said minimum relevance level wherein saidset of weights are associated with said subject's location and frequencyin said document.

In another embodiment, a method may perform analytics to facilitateissue awareness comprising accessing, from an operational database, atleast one profile for a supplier comprising a set of tags, a tier, atier weight, a category, a category weight, a sub-category and asub-category weight. It may also perform the step of accessing, via saidoperational database, an index of metadata associated with a corpora ofelectronically stored content. Next the method may perform byautomatically matching, using a computer machine, a document from saidcorpora to said profile wherein said set of tags associated with saidsupplier profile match a set of terms in said index of metadataassociated with said document. Scores may be derived by calculating,using a computer machine,

-   -   a base document score for said document;    -   for a given supplier, an average base document score by        averaging all said base document scores for a given supplier;    -   a tier content score by averaging said average base document        score for all suppliers within a given tier;    -   a weighted tier score for said tier by applying a tier factor;    -   a subcategory risk score by applying a subcategory risk weight        to a maximum weighted tier score within said subcategory;    -   a category risk score by applying a category risk weight to a        maximum subcategory risk score within said category;    -   a departmental risk score by averaging all category risk scores        within said department.

This embodiment may perform by generating, using a computer machine, atleast one interactive graphical user interface comprising a risk gridhaving a supplier axis and a risk category axis wherein said supplieraxis is organized in a taxonomy with a highest level being adepartmental row, a next level being a category row; a next level beinga subcategory row and a next level comprising a row assigned to eachsupplier falling into that taxonomy and said risk category axis providesa column for each risk category within a set of risk categories; eachcell within said risk grid comprises a representation of a risk scorecalculated for an intersection of said supplier axis level and said riskcategory; each cell within said risk grid may be clicked through toreveal a list of content sources from which said risk score derived; andeach item in said list of content sources may be clicked through toreveal an underlying document for said item.

In another embodiment, a method to perform analytics to facilitate issueawareness may comprise accessing, from an operational database, at leastone profile for a supplier comprising a set of tags, a tier, a tierweight, a category, a category weight, a sub-category and a sub-categoryweight. It may further comprise accessing, via said operationaldatabase, an index of metadata associated with a corpora ofelectronically stored content. It may further comprise automaticallymatching, using a computer machine, at least one document from saidcorpora to said profile wherein said set of tags associated with saidsupplier profile match a set of terms in said index of metadataassociated with said document. It may further comprise calculating,using a computer machine, a base document score for said document; anddetermining, using a computer machine, a score for said supplier with analgorithm using said base document score and said profile for saidsupplier as computer machine input. It may further comprise generating,using a computer machine, at least one interactive graphical userinterface comprising said score for said supplier wherein said at leastone document can be accessed by clicking on an icon representing saidscore for said supplier.

In an embodiment, the previously described method may utilize an indexof metadata comprising a list of risk categories.

In another embodiment, the list of risk categories may comprise ataxonomy of issues including environmental issues, economic issues,societal issues, political issues, technological issues,business-specific issues, and legal issues. A taxonomy is aclassification or categorization of things into a hierarchy.

In another embodiment, the base document score will be lower if a set ofmetadata associated with said base document matches a predetermined listof risk subjects wherein said risk subjects are organized into saidtaxonomy of risk categories.

In another embodiment, the base document score comprises a risk event ifsaid base document score negatively affects said score for saidsupplier.

In another embodiment, a computer-readable medium comprisingcomputer-executable instructions for execution by a computer machine toperform analytics to facilitate issue awareness that when executed,cause the computer machine to receive a query including a supplierprofile. It may access at least one profile for a supplier, from acomputerized database, comprising a set of tags, a tier, a tier weight,a category, a category weight, a sub-category and a sub-category weight.It may access an index of metadata, stored on a computer-readablemedium, associated with a corpora of electronically stored content. Itmay match a document from said corpora to said profile. It may calculatea base content score for said document wherein said set of tags,associated with said supplier, match a set of terms in said indexassociated with said document. It may calculate a tier content score byaveraging said base content score for all suppliers within a given tier.It may calculate a weighted tier score for said tier by applying a tierfactor. It may calculate a subcategory risk score by applying a risksubcategory factor and a subcategory factor to a maximum weighted tierscore within said subcategory. It may calculate a category risk score byapplying a category risk factor to a maximum subcategory risk scorewithin said category. It may calculate a departmental risk score byaveraging all category risk scores within said department. It maygenerate at least one interactive graphical user interface comprising arisk grid having a supplier axis and a risk category axis wherein saidsupplier axis is organized in a taxonomy with the highest level being adepartmental row, the next level being a category row; the next levelbeing a subcategory row and the next level comprising a row assigned toeach supplier falling into that taxonomy; said risk category axisprovides a column for each risk category within a set of riskcategories; each cell within said risk grid comprises a representationof a risk score calculated for an intersection of said supplier axislevel and said risk category; each cell within said risk grid may beclicked through to reveal a list of content sources from which said riskscore derived; and each item in said list of content sources may beclicked through to reveal an underlying document for said item.

In another embodiment, a computer-readable medium comprisingcomputer-executable instructions for execution by a computer machine maygenerate the interactive GUI comprising a risk grid with the additionalfunctionality to allow a user to expand or collapse said supplier axisto reveal or hide a given level within said taxonomy and to expand orcollapse said risk category axis to reveal or hide a set of riskperspectives associated with each risk category when executed.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments set forth in the drawings are illustrative and exemplaryin nature and not intended to limit the subject matter defined by theclaims. The following detailed description of the illustrativeembodiments can be understood when read in conjunction with thefollowing drawings, where like structure is indicated with likereference numerals and in which:

FIG. 1A represents an exemplary dashboard GUI Generated for GraphicalDisplay on a visual sub-system comprising a Technology Risk Grid. Weusually use exemplary in describing drawings . . . .

FIG. 1B represents a dashboard GUI Generated for Graphical Display on avisual sub-system comprising a pop-up risk assessment for a TechnologySupplier.

FIG. 2A represents a dashboard GUI Generated for Graphical Display on avisual sub-system comprising a Technology Risk Grid with accumulated andexpanded views.

FIG. 2B represents a dashboard GUI Generated for Graphical Display on avisual sub-system comprising a pop-up Risk Score/content summary of agiven Risk Type and Risk Perspective for a Supplier.

FIG. 2C represents a second window of a set of content, associated witha source in FIG. 2B's content summary, Generated for Graphical Displayon a visual sub-system.

FIG. 2D represents a GUI Generated for Graphical Display on a visualsub-system comprising a set of content sources aligned with differentRisk Types.

FIG. 3A represents another dashboard GUI Generated for Graphical Displayon a visual sub-system comprising a Procurement Risk Grid withaccumulated and expanded views.

FIGS. 3B(1-4) represent a variety of possible GUIs Generated forGraphical Display on a visual sub-system comprising a set ofSupplier/risk graphs.

FIG. 3C represents of a GUI Generated for Graphical Display on a visualsub-system comprising a heat map.

FIG. 3D represents of a GUI Generated for Graphical Display on a visualsub-system comprising a risk panel.

FIG. 4 represents a GUI Generated for Graphical Display on a visualsubsystem comprising an administrative interface to upload a list ofSuppliers.

FIG. 5A represents a GUI Generated for Graphical Display on a visualsubsystem comprising an administrative interface to disambiguate a listof Suppliers.

FIG. 5B represents a GUI Generated for Graphical Display on a visualsubsystem comprising a second administrative interface to disambiguate alist of Suppliers.

FIG. 5C represents a GUI Generated for Graphical Display on a visualsubsystem comprising a third administrative interface to categorize aSuppliers' Industry.

FIG. 6 represents a GUI Generated for Graphical Display on a visualsubsystem comprising another administrative interface to organize Tagsassociated with a set of Suppliers.

FIG. 7 represents a GUI Generated for Graphical Display on a visualsubsystem comprising an administrative interface for setting andweighting Tags.

FIG. 8 represents a GUI Generated for Graphical Display on a visualsubsystem comprising an administrative interface for setting andweighting Risk Categories

FIG. 9 represents a data structure stored on a computer readable mediumwhich defines a Supplier.

FIG. 10 represents a process flow for uploading and tagging a set ofSuppliers.

FIG. 11 represents a high-level overview of input-processing-output byan embodiment of the system.

FIG. 12 represents an architecture for an embodiment of the system.

FIG. 13-14 represent a series of outputs (bar graphs, heat maps, barcharts, textual summaries) Generated for Graphical Display on a visualsubsystem comprising a comparison of awareness of a subject in a set ofgeographical regions for each month, over a period of several months,against a baseline.

FIGS. 15A-B represent a sample spreadsheet portraying one method ofrolling up scores.

FIG. 16 represents one possible output chart for an embodiment of theinvention.

FIG. 17 represents an embodiment of Computer Machine Input.

DETAILED DESCRIPTION

The drawings, systems and methods described herein relate to analyzing avariety of data and graphically generating conclusions regarding thatdata. As discussed herein, systems and methods allow departments (e.g.,procurement departments) to proactively mitigate risks and discoveropportunities associated their third-party relationships using variousembodiments of the system/method described herein to extract, analyseand connect events, Suppliers, industry, and location may be extractedfrom a corpus of content (including, but not limited to, aggregation ofnews sources, public records, legal content, company profiles, financialsources, industry sources, executive/biographical sources, and licensedcontent sources). These sources (or a subset thereof) can provide thebasis for risk calculations based on operational events (e.g., strikesor technology issues, natural disasters, product recalls, reputationalrisks, changes in commodity pricing, compliance issues, regulatorychanges, and many more). Embodiments may provide the ability to drilldown to even the Supplier's Suppliers which may impact the Suppliers'ability to deliver products.

Embodiments of the system may generate a variety of dashboards, withdrill-down (e.g., click-through, hover, etc.) functionality, rangingfrom a high-level overview of Supplier risks by Risk Category (e.g.,geopolitical, weather, regulatory, compliance, reputational) to morefinite analyses or even the underlying content used to analyze an areaof risk. Embodiments of both the system(s) and the dashboard(s) mayprovide mechanisms/means for prioritizing/alerting the analysis ofrisks/opportunities and their presentation via a dashboard.

Definitions

“Automatically” includes the use of a machine to conduct a particularaction.

“Calculate” includes Automatically determine or ascertain a result usingComputer Machine Input.

“Computer Machine” includes a machine (e.g., desktop, laptop, tablet,smartphone, television, server, as well as other current or futurecomputer machine instantiations) containing a computer processor thathas been specially configured with a set of computer executableinstructions.

“Computer Machine Input” includes input received by a Computer Machinethrough a variety of means (e.g., HTTP, multi-modal entry, databasequery, etc.).

“Generate for Graphical Display” includes to Automatically create, usingComputer Machine Input, an object(s) to be displayed on a GUI (e.g., alisting of hyperlinks, a heat map, a dashboard comprising a table, icon,and color-coding, etc.).

“GUI” or “Graphical User Interface” includes a type of user interfacethat allows users to interact with electronic devices via images (e.g.,maps, grids, panels, etc.) displayed on a visual subsystem (e.g.,desktop monitor, tablet/phone screen, interactive television screen,etc.).

“Metadata” includes to a type of data whose purpose is to provideinformation concerning other data in order to facilitate management andunderstanding. It may be stored in the document internally (e.g. markuplanguage) or it may be stored externally (e.g., via a database such as arelational database with a reference to the source document that may beaccessible via a URL, pointer, or other means).

“NAICS” includes to a system of classification which classifiesestablishments by their primary type of activity whichreplaced/supplemented the Standard Industrial Classification (SIC)starting in 1997.

“OFAC” includes to a sanction list provided by the US Department of theTreasury's Office of Foreign Asset Control (OFAC) which requiresspecific action(s) under US regulations including but not limited tofreezing assets, rejecting transactions and/or reporting potentialmatches to OFAC for instruction and follow-up. Matches typically must bereported to OFAC within 10 days. Lists may include:

“OFAC” includes Specially Designated Nationals (SDN). Non-SDN, includingPalestinian Legislative Council (PLC). Enhanced Sanctioned CountriesOffice of Foreign Assets Control.

“PEP” includes Politically Exposed Persons. On Rosetta, the WorldCompliance PEP file provides a comprehensive database of “PoliticallyExposed Persons” (PEPs), their family members and close associates.

A “Risk Category” comprises a grouping of issues that may have anegative impact on an aspect of operations such as environmental,economic, societal/reputational, political or geo-political, technology,operational, and legal.

A “Risk Dimension/Perspective” comprises a subset of a SupplierCategory. In an embodiment, a Risk Dimension may focus on a Company. Inanother embodiment, a Risk Dimension may focus on an Industry (e.g.,pharmaceutical, high-tech, agriculture). In another embodiment, a RiskDimension may focus on a Location. Risk Dimensions may be portrayedalone or in groups.

A “Risk Event” comprises an event from a Risk Category/Subject that mayhave a negative impact on a Supplier or Supplier Category. These mayinclude environmental events (including, but not limited to, natural andmanmade disasters such as hazards, oil spills, tsunamis, tornados, EPAinvestigation against Suppliers, pollution, and more), economic events(news of layoffs, plant closings, bankruptcy, executive moves, macroindicators such as recessional impacts across industries, volatility ofcommodity prices, and other predictors of company distress),societal/reputational events (news impacting Supplier's brand/reputationincluding child labor, human rights violations, product recalls,compensation issues, or other ethical issues), political/geo-politicalevents (country-based risk upon leadership changes, riots, terrorism),technology/operational events (cybertheft and other computer crimes,strikes, port closures, product quality or service issues, productrecalls and more), and legal/litigation events (patent infringement,customer/shareholder lawsuits, government agency investigations orinspections, compliance issues, sanctions, watch lists, and other legalproblems). Risk events are not necessarily exclusive to a single RiskCategory/Subject. Risk Events may be selected from a predefined taxonomy(e.g., LexisNexis SmartTagging) that may be updated as new terms areidentified.

A “Risk Subject” comprises a grouping of sub-issues that can be mappedinto a taxonomy structure based on Risk Categories. A Risk Subject maymap to one or more Risk Categories. In a preferred grouping or apreferred taxonomy, there may be hundreds or even thousands of possiblesubjects including, but not limited to, or variants of the following:product recalls, bankruptcy, boycotts, bribery, natural disasters,investigations, chemical & biological terrorism, slavery or forcedlabor, negative news—Business, Identity Theft, Corporate Insolvency,Plant Closures, Eco-terrorism, Pesticides, Layoffs, Internet Crime,Human Rights Violations, Wrongful Termination, Wage Violations, ChildLabor, Lockout, Product Quality Issues, Sweatshops, Strikes, ConflictMinerals Violations, False or Misleading Advertising, Labor Unions/LaborProblems, Pollution, Pharmaceutical Drug and Devices, Adverse Drug EventReporting, Human Trafficking, Patent Infringement, Drug & Medical DeviceApproval, Toxic & Hazardous Substances, Geo-Political Risks, FDAApprovals, Corporate Insolvency, Products Liability, Ethical Issues,Biological Contaminants, Professional Negligence, FDA Review,Carcinogens, Human Exposure Assessment, Pharmaceutical Drug and DevicesLitigation, Mergers & Acquisitions, Safety and Workplace Health Issues,Executive Moves, Hazardous Waste, Operational Issues, Commodities,Agricultural Wastes, Sexual Harassment in Employment, Litigation,Tsunami, FCPA/Anti-Corruption Violations, Financial Distress, HeavyMetals & Toxic Minerals, Embargoes & Sanctions, and more. A set(including subsets, expansions and variants) of subjects/tags/taxonomiessuch as those used in the LexisNexis Smart Indexing may be mapped to theRisk Categories. Any given Risk Subject may also be a parent to one ormore sub-Risk Subjects. Risk Subjects may be coded with a set of searchterms, weights, sensitivities, and filters to prioritize the riskpresented by a particular source within a corpus of content. This mayfurther inform the calculation of the Risk Score. Also, a given RiskSubject may be coded to surface regardless of the Risk Score calculatedif other criteria are fulfilled (e.g., a must-surface tag or a deathmatch tag).

A “Risk Rating/Score” comprises an assessment of the level of riskassociated with one or more Risk Categories or Risk Events. It may bepresented in numeric, color-coded, shaded or other formats.

“Risk Weight” comprises a Risk Category attribute which defines a givenRisk Category's relative level of importance against other RiskCategories.

“Smart Indexing” comprises a methodology by which subject matter expertsand information professionals create vocabularies and the algorithmicrules governing the application of Tags to a content item.

A “Supplier Category” comprises an aspect of procurement/contentoperations.

A “Supplier Category Tag” includes a Tag which associates a particularSupplier Category with a Supplier. It may or may not include a TagWeight defining the importance of that Category in assessing a specificSupplier.

“Surfacing” comprises a variety of methodologies employed to madecontent stored in servers and connected to the Internet (or othernetwork system) available for further review or selection. Content madeavailable through surfacing may comprise a hierarchy ofcomputer-selectable links delivered as a result set to a query.

“Tag” includes metadata/keywords used to classify information. Tags maybe organized in a taxonomy or hierarchy which includes nestedcomponents. Tags may also include an attribute to allow a user to definea Tag Weight to be associated with the Tag.

“Tag Weight” includes a Tag attribute which defines a given Tag'srelative level of importance against other Tags.

“Zeitgeist” comprises the spirit, attitude, or general awareness of aspecific issue within a specific time or period especially as it isreflected in literature (e.g., newspapers and other published sources).

Dashboards

Referring to FIGS. 1A-1B, embodiments of the systems may Generate forGraphical Display different types of dashboard views (100 and etc.) thatcan convey a variety of risk assessments both vertically (within aspecific industry, e.g., technology) and horizontally (across multipletypes of Suppliers, e.g., hardware, software, services, etc.). Anembodiment may also provide the ability to Generate for GraphicalDisplay a representation of different layers of risk from a high-levelrepresentation depicting a comparison of Risk Scores for multipleSupplier Categories/Suppliers (see, FIG. 1A) to more focused snapshot(see, FIG. 1B) of Risk Scores associated with a particular Supplier. Anembodiment may have the ability to Generate for Graphical Displaydrilled-down or consolidated/accumulated dashboard views to provide auser with varying levels of information.

Referring to FIG. 1A, this embodiment Calculates then Generates forGraphical Display a set of Risk Scores (140) at the intersections ofSupplier Categories (120) and Risk Categories (130) for a higher levelSupply Category (110). An embodiment of this dashboard may also allowexpansion of a set of Supplier Categories (120) to Generate forGraphical Display a Risk Score (140) across Risk Categories (130) forboth Supplier Categories (120) and hierarchically nested childrenSuppliers (150).

Referring to FIG. 1B, an embodiment may utilize a pop-up (160) todisplay Risk Scores (170) for a specific Supplier (190). A pop-up (160)for “Telecom Bill Auditing” (190) displays a set of four color-codedRisk Categories (171-174)(in this example, shading designates the levelof severity although other mechanisms, such as color, may be utilized toconvey that information): Environmental, Technology, Corporate-BusinessSpecific and Legal.

Referring to FIG. 2A, another embodiment may Generate for GraphicalDisplay a dashboard (100) in a table format which allows a user tointeractively drill down to more finite information by parsing each RiskCategory (230) (e.g., Environmental) into a set of RiskDimensions/Perspectives (e.g., Company (231)/Industry (232)/Location(233)). At the Supplier Category level (240)(e.g. Content Operations),the Risk Dimensions/Perspectives (230) may be filled in to indicate anassociated Risk Score (234). At the individual Supplier level(270)(e.g., 365 Media Group), each cell may provide an iconic indicator(260) (e.g., a bubble) of the Risk Score to visually communicate thegreater degree of granularity. It should be kept in mind that otherschemes, capable of being scanned easily by a reviewer, could also beemployed.

Referring to FIG. 2B, further data may be accessed by clicking on theiconic indicator (260) which will Generate for Graphical Display aninteractive summary or set of links (276), for a set of Surfaced contentsources (275) for a given Supplier (e.g., Cypress SemiconductorCorporation) and their associated Risk Scores (274) within a given RiskType (e.g., Economic) within a Category of Suppliers (e.g., Telephone)for a given Department (e.g., Technology). Referring to FIG. 2C, a linkin a set of links may be clicked through to Generate for GraphicalDisplay a content window (280) associated with that headline (290).

Referring back to FIG. 2B, it can be seen the Risk Score (274) forCypress Semiconductor Corporation Calculated for the Economic RiskPerspective and Company Risk Dimension may be based on three Risk Scoresof 50 and two Risk Scores (274) designated with an exclamation point (toindicate automatic surfacing). Referring to FIG. 2C, an additionalpop-up window (280) is Generated for Graphical Display to provide thecontent of a given content source (e.g., “Heath Effects of RadiationPoisoning”). Referring to FIG. 2D, a set of links may be Generated forGraphical Display to provide a user with a set of content, links, orother information (e.g., links to government sites, NGO/watchdog sites,weather sites) associated with a given Risk Category (e.g., Economic,Environmental, Geo-Political, Legal/Compliance).

Additional icons may be Generated for Graphical Display to indicatelate-breaking news (e.g., a newspaper icon for new sources added withina certain number of runs or other pre-configured amount of time such aswithin the last twenty-four hours) or more intense risk events (e.g.,two standard deviations more than normal press coverage in a given timeperiod indicated via a flame icon). In additional embodiments, a list ofunderlying content may be filtered to show a subset of sources. Forexample, negative news content may be identified by running a querycomprising a selection of negative impact terms against a result set topull out articles with those terms present:

-   -   accus! or acquit! or allegation or allege or alleged or arraign!        or arrest! or bankrupt! or barred or bid rig! or breach! or        brib! or charged or collud! or collus! or conspir! or        controvers! or convict! or corrupt! or cosa nostra or crime or        criminal or debar! or decept! or defraud! or derogatory or        disciplin! or discriminat! or embezz! or evad! or evasion or        extort! or felon! or fined or fired or fraud! or grand jury or        guilty or harass! or illegal or indict! or investigat! or jail        or kickback or kill! or kiting or larceny or launder! or lawsuit        or litigat! or mafia or misappropriat! or misdemeanor or        misrepresent! or negligen! or notorious or offense or organized        crime or ousted or overstat! or parole or payoff or ponzi or        pornography or price fix! or probe or prosecuted or racketeer!        or revok! or sanction! or scam or scammed or scandal or scheme!        or scheming or sentenc! or settle! or suit or suspend! or        suspension or swindl! or terminate or terminated or terrorism or        terrorist or testif! or testim! or verdict or violat!

One of skill in the art will appreciate the possible modifications tosuch a query including performance of an inverse positive filtering.Once a document is accessed (e.g., a user viewing the cite list clickson a link), a user may choose to save, print, or share the specifieddocument in a variety of formats (e.g., txt, pdf, rft, HTML, etc.)

Referring to FIG. 3A, an embodiment Generates for Graphical Displayanother variation of a dashboard (300) that may be expanded or collapsedto display varying levels of data. In an embodiment, a first column(310) provides a set of Supplier Categories (311) that may be expandedinto a taxonomy of more finite Supplier Categories (240) or even down tothe level of individual Suppliers (270). An embodiment Calculates andGenerates for Graphical Display a summary view (320) of the Risk Scoreacross all Risk Categories (230) for a given Supplier (270) or SupplierCategory (240 or 311). A risk level filter may be used to tailor resultsto a specific set of Risk Scores. An additional toggle (e.g. a bubblepositioned by or near a given Supplier, 270) may permit the user to addor delete specific Suppliers from an alert list.

A Risk Score (330) may be indicated through a variety of one or moremechanisms including, but not limited to, color-coding, shading ornumeric risk level. For instance, a color-coded bar may be displayed ora numeric score may be provided through a pop-up icon/window (330)activated by hovering over the color-coded/shaded bar (340, 350). RiskScores across a set of Risk Categories (230) may be Calculated andGenerated for Graphical Display. Finally, a Risk Category Column may beexpanded (individually or in groups) to Calculate and Generate forGraphical Display a Risk Score (330) associated with a set of RiskDimensions (231-233, 234) within a Risk Category (230).

Referring again to an embodiment represented by FIG. 3A, a parentSupplier Category entitled “Procurement” (311) displays a (medium-high)Risk Score in its summary column (321). The Summary Column (321) may beexpanded to Calculate and Generate for Graphical Display an expandedsummary column (322) for each Risk Category (230). Expanding theSupplier Category column Generates for Graphical Display the childrenSuppliers (270) and/or Supplier Categories (240). Risk Scores (330, 331,etc.) for each child are Calculated and Generated for Graphical Displayfor the Summary and Risk Category columns. A user may perceive that new(unread or not previously accessed) underlying content sources areavailable for each Risk Score based on an iconic indicator (332)included in a Risk Score cell (e.g., an asterisk within theshaded/colored bar).

Referring to FIG. 3A, a Risk Category column may be expanded toCalculate and Generate for Graphical Display a set of Risk Scores for aset of Risk Dimensions (e.g., Company (231)/Industry (232)/Location(233)) within that Risk Category. In an embodiment, if a given RiskCategory (230)(economic, technological, etc.) is collapsed, then theRisk Score (330, 331) may represent the highest score from each of thethree Risk Dimensions (collectively, 234) displayed. Thus, in thisembodiment, the collapsed Risk Category bar will be red if the companyis orange, industry is red and location is green. It can be appreciatedthat other representation schemes (e.g. averaging the Risk Dimensionscores to provide the Risk Category color) may be possible.

Referring to FIGS. 3B(1-4)-D, alternative embodiments may Calculate andGenerate for Graphical Display Risk Scores output other than a riskgrid. FIGS. 3B(1-4) represent an embodiment which Calculates andGenerates for Graphical Display risk over a period of time for aSupplier Category(ies)/Supplier(s)/Risk Category(ies)/etc. in the formof various graphs. FIG. 3C represents an embodiment which Calculates andGraphical Generates a Risk Score in the form of a color-coded heat mapwhere the Risk Score is aligned with a Location associated with a givenrisk. FIG. 3D represents an embodiment which Calculates and Generatesfor Graphical Displays a Risk Score summary for a given category such asa Supplier Category or a Risk Category (e.g., Technology although panelsmay be Generated for Graphical Display for other categories) in the formof a summary panel.

Embodiments of the system may provide alerts via a variety ofcommunication mechanisms including, but not limited to email and textmessages.

Setup—Loading Content

Referring to FIG. 4, an administrative GUI (400) is generated forGraphical Display to allow a user to enter or browse for a formattedfile of Supplier information to upload into the system, for example, a.CSV file (410). In other embodiments, a single Supplier may be enteredat a time through a web form. Once the file is uploaded, a user will seean accumulated set of Suppliers. An “Include Location” check box (415)for each Supplier may be checked. This allows a user to include locationinformation in the disambiguation process. After the upload iscompleted, the data can be disambiguated. The system may also be set upto include location information by default and allow the user to excludeas desired.

Setup—Disambiguation (420)

Referring to FIG. 5A, an embodiment of the system provides anadministrative GUI (400) to allow a user to resolve and/or validateSupplier name(s) and/or industry(ies) and/or location(s). An embodimentlooks for an exact match (510) in a corporate database which may beprovided through a third party via a request through an applicationprogramming interface (API—e.g., LexisNexis Dossier Suite or anotherthird-party supplier platform with similar functionality). In thisembodiment, if a macro event, such as geo-political unrest or naturaldisaster occurs in a particular region, the system immediatelyidentifies which Suppliers are impacted.

An embodiment may then request confirmation from the user. Referring toFIG. 5B, if multiple matches are found (matches may be exact orsimilar), a user may select the one of interest (520) via a secondinteractive GUI (540) Generated for Graphical Display. If no matches arefound, then the system may prompt the user for different information(530). An indication may be provided if an entry has been successfullydisambiguated and validated. Validation requests may be color-coded. Anembodiment of the system may provide information via a dashboardincluding a color-coding scheme representing the current state ofdisambiguation/validation and pop-ups to allow selection of alternativeSupplier/Industry/Location. Hover functionality may provide a pop-up ofCompany code, Industry code and/or Location code. A user may customizethe disambiguation process (420) to focus on only a subset ofSupplier/Industry/Location input. Omitting a tag is also possible andmay result in a broader net for capturing data or be irrelevant (e.g.,for a service-based company with multiple locations in the manpowercategory). For instance, if the Location tag is omitted, the user mayreceive data for all locations of a given Supplier as opposed to itsoperations in a specific geographic location (e.g., city, state, region,country, etc.). In an alternative embodiment, a system can also beconfigured to track a subset of a given Supplier's locations orindustries.

Referring to FIG. 5C, an embodiment Generates for Graphical Displayanother administrative GUI (550) to allow for the disambiguation of aparticular industry. If a user clicks on the information icon, a pop-upwith a more detailed explanation of that industry may be provided.Location tagging may be similarly disambiguated.

Setup—Tagging

Referring to FIG. 6, an embodiment of the setup functionality (600) isprovided. Once a Supplier(s) has been disambiguated and validated, theremay be provided means for viewing and editing the list of Suppliers (ora group thereof) as well as customizing a set of Tags to be used asComputer Machine Input by an embodiment of the system to determine howSuppliers may be categorized or rolled up. For instance, General Motorscould have a group of suppliers titled Brake Suppliers, which rolls upto Auto Parts, which rolls up to Chevrolet, etc.

In an embodiment, Supplier data may be tagged against a set of indexingterms as it is loaded (e.g., Lexis Nexis Smart Indexing) for Company,Location, and Industry. Data may also be matched against a database ofcountry risk data comprising country risk scores in areas such asbribery, corruption, geopolitical unrest, natural disasters, etc. Datamay be used to generate heat maps (FIG. 3C) to indicate risks based uponSupplier location. Such maps may include multiple layers, includingstatic information (e.g., location on a fault line or location of anarea with a high risk-index for child labor). It may also be used toCalculate the base Risk Score for the location scores in the dashboardmatrix.

Referring to FIG. 7, the Tags associated with a Supplier may behierarchically arranged (720) in a tree structure and weighted (730)using a GUI (700) generated by an embodiment of the system. Referring toFIG. 8, another generated GUI (800) allows Risk Weights (810) to be setfor a given Supplier. In an embodiment, weights may be set in the rangeof 1-5 with each point being worth 20% of the total (it will beappreciated that many weighting configurations may be employed). Weightsmay be used to determine the sensitivity to give to each supplier orSupplier Category, based upon its relative importance to a company.Weightings may help determine which content to Surface as well as toCalculate one or more Risk Scores. One or more Tags may beselected/deleted/re-assigned for a given Supplier by selecting thecheckbox associated with a given Tag. This lets the user control howembodiments of the system Generate for Graphical Display a givendashboard.

Referring to FIG. 9, a given Supplier may be defined as Computer MachineInput via a data structure including, but not limited to, the followingattributes or a subset thereof, for the purpose of associating contentand Calculating a variety of Risk Scores:

-   -   Supplier ID (910)    -   Supplier Name (official name for this Supplier) (915)    -   Alternative Names (if applicable, e.g., Big Blue for IBM) (920)    -   Company Identifying Information (including but not limited to HQ        location, ticker, D&B number, website URL, etc.) (925) (e.g.,        using LexisNexis Corporate Affiliations Data)    -   Ultimate Corporate Parent (even if there are multiple tiers        between a given entity and its ultimate parent) (930)    -   Intermediate Parents (a user may configure their query to        include ultimate or intermediate parents in the risk        calculation) (935)    -   Taxonomy Derived Tags (950) may be chosen from one or more        taxonomies associated with the content being mined (e.g., 3500        tags in LexisNexis SmartIndexing or 100 tags in NAICS):    -   Supplier Category (951) may be defined in groupings of various        sub-categories (e.g., travel services, transportation) as well        as by level of importance (for instance, if office supplies is a        lower category than the HR platform). Multiple layers of        Supplier Categories may be nested (e.g., Supplier Category,        Department, Division, etc.).    -   Product Description (952)    -   Manufacturing Facilities or Key Locations (953, 954, 955) may be        used to match Suppliers to the location risks they may incur—in        an embodiment, all locations that are utilized in the supply        chain may be included to identify any disruptions along the        line.    -   Supplier Tier (a designation of the importance of a given        Supplier) (940). Tier weighting may be used to define how        important a given Supplier is (e.g., Tier 1 may be used to        indicate that a product cannot be made without that particular        Supplier). Tier 1 Suppliers may have a unique offering or they        may have customized their offering particularly to meet a need.        If Tier 1 Suppliers had a disruption in their business        operations, their upstream client would undergo considerable        costs, efforts and possibility inability to produce additional        automobiles until the break in the supply chain is fixed. Tier 2        Suppliers may be important, but not mission critical and Tier 3        Suppliers may be the third tier of important Suppliers. Any        additional Suppliers may consist of Suppliers of easily        replaceable products like office supplies or travel agencies.

Any of these attributes may be coded as a Supplier Category Tag.

FIG. 10 provides a global overview of the tagging process (1000) acrossa set of administrative modules such as Common Dossier Service (CDS) andIndexing Metadata Service (IMS) and an operational database (e.g.,MarkLogic or HPCC—high performance computing cluster). An AdministrativeUser may upload a list of Supplies (1010) (e.g., through a bulk upload).Tags may be added as part of the bulk upload along with weighting foreach Tag (1020). In the CDS component, validation of the company namemay be performed by checking Dossier IDs and other third party databases(e.g., NAICS or LexisNexis Industry Code) (1030). Validation of industryand location may occur in an IMS component although a system could beconfigured to combine/split validation into a single or alternativemodules. GeoCodes may also be established for location matches byinteracting with the IMS component (1040). Once the Supplier has beenvalidated, the core information (dossier ID, NAICS industry code, andGeoCode) may be stored for comparison against content stored in theoperational database. This stored information may form apre-disambiguated supplier list that can be used to more quicklydisambiguate future entries. If no matches are found, admin user cansupply additional terms for us to match the documents for that supplier.

Searching

An embodiment of the system may access a corpus of content comprising anaggregation of sources (or subset thereof) including, but not limitedto, news, legal analyses, and updates, business editorials, publicrecords (e.g., PEP and OFAC) and other sources from around the world. Athird party provider may provide the corpus of content (e.g., LexisNexisor a third-party provider such as the New York Times) or the corpus maybe available through other means.

In an embodiment, content sources in the corpus may be analyzed andtagged according to a predefined taxonomy (e.g., LexisNexisSmartIndexing) using a rules-based automated system that may classifydocuments for subject, company, industry, people, location or otherclassification. The tagging may be geared toward a specific slant bychoosing a subset of Tags available in a given taxonomy (e.g., Tagsassociated with risk analysis). Tagging may be performed as a separatestep on a given corpus or executed contemporaneously as new content isreceived.

The chosen taxonomy methodology may be supplemented by extraction andanalytics tools for the evaluation of big data (whether structured orunstructured) (e.g., NetOwl), to recognize events and associate themwith the Suppliers who may potentially be impacted. An embodimentsearches the content corpus and/or content Tags (which may be stored ona database, extracted from a news feed, or provided through some othercontent delivery mechanism) for Supplier Tags (e.g., a Supplier's uniqueinformation like ‘CCT’ code (for company), ‘Dossier ID’ (for company),‘NAILS’ (for industry), and ‘GeoCode’ (for location)).

In an embodiment, if a Supplier's company name, industry and/or location(or expected variations thereof) are found in a content source with aRisk Event, a match may be made, and the content may be scored andsurfaced to the dashboard.

Scoring

One of skill in the art will appreciate that there are many ways to loadcontent into an operational database server (e.g., MarkLogic 5, HP)including using MarkLogic XQuery codes, RecordLoader, WebDAV,Information Studio (or Info API), REST, XCC and others. It may also bedesirable to create a custom loader to pre-process content prior toutilizing a commercial or open source record loader. Preferably, themechanisms used to load content will allow the receipt of either largeXML files or zip files with many XML files by breaking them up intosmall documents; loading XML directly from a zip archive; applyingtransformation to change the format of the documents; resuminginterrupted loads; and running multiple parallel loads. Various toolsknown to those of skill in the art are available for these purposesincluding, but not limited to, java utilities and/or lightweight javaprocesses (e.g., BatchProcessor or Total Patent's RecordLoader) andMarkLogic's Information Studio (i.e., Info API).

In an embodiment, once a match occurs, the corresponding Risk Event maybe scored. An embodiment may Calculate a Risk Score using Tag Weightsand/or Risk Category Weights. The result set Generated for GraphicalDisplay may be more finely tuned by slicing the data along a particularLocation, Industry, Supplier and/or Risk Category.

A user may weight a defined Risk Category (e.g., reputational risk ismore important than environmental risk so reputational risk receives ahigher weight). Risk Categories may default to equal weighting ingenerating a summary score (e.g., an average across the columns) but theweights may be configured to produce a specialized score. In this way, agiven Risk Category may be of less importance to the overall score oreven eliminated entirely (e.g., by setting the weight to “0” or someother way nulling that factor). Risk Scores may affect the order ofdocuments presented in a dashboard.

A Base Score based on a document's sentiment may be calculated todetermine overall impact (negative or positive) of an information sourceto the Risk Score. More detailed taxonomies may be created to classifydocuments on a more granular level (e.g., paragraph or sentence)especially when multiple companies/industries/locations are named in aspecific document. A third party product such as LexisNexis AnalyticalSolution may be used for this purpose.

Spend Classification Weighting

In an embodiment, a user may define a highest tier (e.g., Supplier) aswell as specific spend categories (e.g., IT spend) and even spendsub-categories (e.g., desktop hardware).

Once a user defines their spend categories they may then assign everySupplier to a spend category or sub-category. A user may then weighteach of these categories and sub-categories:

Cumulative Top Tier Category Weighting Weighting Global Procurement IT30% 30% Services 25% 55% Marketing 35% 90% Travel 10% 100% 

These adjustments allow a customer to define the relative importance ofone area of spend or another into the tool.

Suppliers may be given a weighting of 1 to 3 where 1 is the highest(most important) weighting. These weightings may also be referred to asSuppliers Tiers (or “Tiers”). These weightings may be embedded in aCalculation to adjust a base score. For example, the followingadjustments might be effect of a given tier:

Tier 1 Base Score * 125% Tier 2 Base Score * 100% Tier 3 Base Score *80%

Subcategory Score

Score roll-up may include a leveling Calculation since there may be moreSuppliers in a given Supplier Category. For example, because there aremore Suppliers in Subcategory 2, Subcategory 2 appears to have the mostrisk when in fact both Suppliers in Subcategory 1 have a much morehigher Base Score.

Supplier Subcategory 1 Subcategory 2 A 95 B 97 C 23 D 45 E 51 F 4 G 37 H61 Total Category Scores 92 261

In an embodiment, an adjustment may be Calculated against the Base Scorefor a given Supplier that is outside of two standard deviations from theMedian (e.g., standard deviation adjustment of 40% that is either addedor subtracted from the Base Score) to compensate when a given Supplierhas a much higher Base Score than its peer Suppliers in the subcategory.Referring to the following table:

Supplier Base Score Adjustment A 23 1 B 16 1 C 69 1 D 18 1 E 27 1 F 851.4

Based on this set of Base scores, the median is 25 and the standarddeviation is 27.03 (these were Calculated using the delivered median andstandard deviation formulas in Excel although other methodologies may beavailable). Therefore, the standard deviation range is 0 to 79.06.(25−(27.03*2)) (negative ranges may be normalized to a score of 0) to79.06 (25+(27.03*2). Because Supplier F is the only Supplier outside ofthis range and they are outside the range on the upper end, they have aMedian Adjustment of +40%.

Adjusted Base Score

Once a Supplier's Tier Adjustment and Median Adjustment are calculated,the adjusted Base Score may be calculated. In an embodiment, the RiskCategory Weighting and Supplier Tier adjustment may be Calculatedaccording to the following formula at the Supplier level for each RiskCategory:Base Score*Supplier Tier Adjustment*Median Adjustment*Risk CategoryWeighting=Adjusted Base Score.

The average of all of these scores for a given category may becalculated to create the Category Summary Score.

Total Supplier Score

The Total Supplier Score may be the sum of all Adjusted Base Scores foreach Risk Category for a given Supplier. For example:Total Supplier Score=(Adjusted Base Score for SocialResponsibility)+(Adjusted Base Score for Environmental)+(Adjusted BaseScore for Geo-political)+(Adjusted Base Score for Economic)+(AdjustedBase Score for Operations/Technology).

Total Subcategory Score

The Total Subcategory Score may be the sum of all Averaged Adjusted BaseScores for each sub-category multiplied by their Spend ClassificationWeighting for a given category. For example:

Supplier Total SupplierScore A 41.06 B 33.35 C 34.00 D 39.68 E 20.08 F84.60 Average 42.13 Spend Classification 10% Total Subcategory Score4.21

Total Category Score may be the sum of all Total Subcategory Scores.

Total Score may be the sum of all Total Category Scores.

Totaling scores may be Calculated at both the Risk Category and Spendcategory level as well as for every Supplier and every Supplier by RiskCategory.

In another embodiment, score-rollup may proceed according to anembodiment depicted in FIGS. 15A-B. Referring to F4 (e.g., DepartmentalScore), an average may be taken of cells F5 and F22-F25 (Categories A,B-E).

A Category Score may be Calculated by rolling up the (Maximum Score fromall of its Subcategories) multiplied by (a factor comprising thatCategory's weight divided by the number of Categories available).

A Subcategory Score may be Calculated by rolling up the maximum score ofall the Subcategories multiplied by two factors:

-   -   The weight given to the overall risk category (here, Social        Responsibility at Weight 5) divided by the number of risk        categories under consideration; and    -   The ratio of that given subcategory over the total number of        subcategories.

Tier 1 Average Content Score may be an average of the content scores forall Suppliers within a given tier. It may be adjusted by a factor ofthat tier's weight divided by the number of possible tiers.

Results from the various scoring methodologies/systems may then be usedas Computer Machine Input to Generate Graphical Displays whichcommunicate that information in an efficient manner.

Architecture

In an embodiment, the system may be architected as a stand-alone system.In another embodiment, it may be installed directly into a workflow as aplug-in.

Referring to an embodiment depicted in FIG. 12 (1200), an operationaldatabase may receive documents from a variety of sources (e.g., DataWarehouse—1201, On-going Loader—1202, etc.). A front-end Sentiment-BasedRisk Analyzer (1210) (SBRA) (e.g., RESTful web service) may performsearch and retrieval of scored data/rollup. A back-end SBRA (e.g.,runtime JAVA component) may perform scoring by applying an algorithm tocalculate a base score for each document. Additional modules for billingand alerting (1212) may be provided. A component (1230) provides theadministrative module, the dashboard module, and the search/retrievalmodule for rolling/scoring data requested from the Analyzer component(1210).

A .NET App may communicate with the IMS and CDS via IWS (1204) forLocation, Company and Industry validation (1205-1206). An embodiment mayprovide communication means between a Sentiment Based Risk Analyzer(1210) and a backend database (e.g., MarkLogic) (1220). Suchcommunication may take place over the Internet utilizing an HTTP server(1221). Embodiments may be coded in Java using an MVC (Model, View andController) hierarchy to abstract the complexities of the differentparts of application. This application may be run on a .NETplatform/framework. In an embodiment, the .NET application may run on adifferent system rather than an instance of MarkLogic server. It mayutilize the MarkLogic built-in ‘XCC’ (Xml Contentbase Connector) (1207)client Java libraries to communicate a database via a MarkLogic XDBCserver (1221). Embodiments may take advantage of built-in logic such asconnection pooling to automatically create and release connections to anoperational server (e.g. MarkLogic server) (1220), automatically poolconnections, and handle multiple requests efficiently. .NET may be athin layer and it may submit XQuery requests for inserting, selecting,updating and deleting data against a database. SMAPI related errorlogging and instrumentation may be incorporated.

Index terms may be identified to Surface risk events. Risks may berolled into Risk Categories, Risk Subcategories and Suppliers, withscores providing Computer Machine Input to Generate for GraphicalDisplay which categories of Suppliers may be at risk. Other content suchas OFAC, PEP and various watchlists may also be monitored and surfaced.Embodiments may also watch for commodity and raw materialpricing/futures and follow a company's stock trends using an integratedhistorical quotes offering (e.g., LN SunGard). Users could click toexpand a category or subcategory of interest and procurement teams couldview the dashboard from within another system (e.g., Ariba).

Referring to an embodiment depicted in FIG. 11 (1100), preprocessing mayinclude developing input for the operational database (1110) byconverting feeds (e.g., zip files, raw XML) (1120) via style sheets(1130) to produce a uniformly formatted XML (1140), converting Supplierlists and Risk Category Weights (1125) from a spreadsheet or other inputformat to a text file (1135) and then to XML (1145), and converting anindex of negative terms (1127) and segment search syntax into text(1137) and then XML (1147). News extracted using JAVA utilitiesprocesses based on negative index terms (1150) for entities of eachSupplier may be loaded into a first snapshot. Using JAVA utilitiesprocesses (1160), Risk Scores may be Calculated for each document into asecond snapshot. Finally, applying Risk Category weighting as well asextracting all documents with automatic roll-up matches (1170), theembodiment may Generate for Graphical Display a third snapshotcomprising the a dashboard view of the final scores.

Billing Mechanism

Embodiments of the system may generate billing events to calculateroyalties/fees associated with the use of content to calculate RiskScores or to provide views of the content. Royalty records may begenerated by weighting the type of access differently (e.g., contentused in the generation of a Risk Score Calculated by the system versuscontent actually viewed versus content used to generate an alert basedon a user's configuration).

Zeitgeist Tracking Mechanism

Another embodiment of the invention may Calculate and Generate forGraphical Display a level of awareness for a subject within both ageographic region and within a time range. An awareness index may bedeveloped by assessing article volume in a specific region on a definedsubject by comparing two or more time frames (the time period for whichone wishes to assess awareness against a baseline timeframe, e.g., 18months). Computer Machine Input may include raw or normalized volumetricdata captured into one or more spreadsheets (e.g. Excel), as depicted inFIG. 17, which outlines hits against the number of publications within ageographic subset that were searched over the past year. In anembodiment, content sources within a corpus may already be tagged withgeographic indicators of the source of the data (e.g., the region ofpublication). Free-text search on a subject that is not already includedin the tagged corpus may be searched using semantic equivalents via athesaurus function. In an alternative embodiment, the system mayGenerate for Graphical Display an administrative interface to allow auser to weight the relevance of that term in a given document based onterm frequency, weight, and location. If a user sets a higher relevancescore, a more discrete return set may result. For example, an index canbe based on all articles with a >50% relevance match for a givensubject, or could be refined to only identify articles with a >70or >90% relevance score, if the user was interested in a more focusedindex.

In an embodiment, the system may present pre-set geographic sourcecompilations or allow a user to define a narrower subset of sources viaan interactive GUI Generated for Graphical Display.

Referring to FIGS. 13-14, a wide range of outputs (bar graphs, heatmaps, bar charts, textual summaries) may be Generated for GraphicalDisplay on a visual subsystem. Embodiments may Generate for GraphicalDisplay a comparison of awareness of a subject in a set of geographicalregions for each month, over a period of several months, against abaseline which represents an average of the entire time period.Embodiments may Generate for Graphical Display a timeline of awareness.

Referring to the table in FIG. 16, a Calculation may be performed todetermine the variance, on a monthly basis, against a baseline score.These monthly volumes may be tracked to identify broad trends inawareness levels, which can be influenced high profile cases, events orcampaigns. When the Index falls below the baseline average score thenawareness of a topic within a geographic areas can be deemed to be belowtrend and when it rises above the baseline average score then awarenessof a topic can be said to be above trend. Accompanying graphics mayfurther highlight such trends (e.g., arrow position).

Further data may be accessed by clicking on an interactive icon whichwill Generate for Graphical Display an interactive summary or set oflinks (270), for a set of Surfaced content sources for a givenSubject/Geographic Region. A link within the set of links may be clickedthrough to Generate for Graphical Display a content window associatedwith that headline. This enables the user to read the underlyingarticles contributing to an index score within the native applicationrather than having to visit multiple external websites where links mayhave expired or be blocked by firewalls or other access controlmechanisms.

Although disclosed embodiments have been illustrated in the accompanyingdrawings and described in the foregoing description, it will beunderstood that embodiments are not limited to the disclosed examples,but are capable of numerous rearrangements, modifications, andsubstitutions without departing from the disclosed embodiments as setforth and defined by the following claims. The diagrams andrepresentations of output Generated for Graphical Display are allprovided for exemplary purposes. The illustrative diagrams and chartsmay depict process steps or blocks that may represent modules, segments,or portions of code that include one or more executable instructions forimplementing specific logical functions or steps in the process upon aComputer Machine. In various embodiments, described processing steps maybe performed in varying order, serially or in parallel. Alternativeimplementations are possible and may be made by simple design choice orbased on, for example, considerations of function, purpose, conformanceto standard, legacy structure, user interface design, and the like.Additionally, execution of some functionality, which would be consideredwithin the ambit of one skilled in the art, may be omitted withoutdeparting from embodiments disclosed herein.

Aspects of the disclosed embodiments may be implemented in software,hardware, firmware, or a combination thereof. The various elements ofthe system, either individually or in combination, may be implemented asa computer program product tangibly embodied in a machine-readablestorage device for execution by a processing unit. Various steps ofembodiments may be performed by a computer processor executing a programtangibly embodied on a computer-readable medium to perform functions byoperating on input and generating output. The computer-readable mediummay be, for example, a memory, a transportable medium such as a compactdisk, a floppy disk, or a diskette, such that a computer programembodying aspects of the disclosed embodiments can be loaded onto acomputer. The computer program is not limited to any particularembodiment, and may, for example, be implemented in an operating system,application program, foreground or background process, or anycombination thereof, executing on a single processor or multipleprocessors. Additionally, various steps of embodiments may provide oneor more data structures generated, produced, received, or otherwiseimplemented on a computer-readable medium, such as a memory. Thesecapabilities may be performed in the current manner or in a distributedmanner and on, or via, any device able to provide and/or receiveinformation. Still further, although depicted in a particular manner, agreater or lesser number of modules and connections can be utilized withthe present disclosure in order to implement or perform the variousembodiments, to provide additional known features to presentembodiments, and/or to make disclosed embodiments more efficient. Also,the information sent between various modules can be sent between themodules via at least one of a data network, an Internet Protocolnetwork, a wireless source, and a wired source and via a plurality ofprotocols.

What is claimed is:
 1. A system configured to perform analytics tofacilitate issue awareness, the system comprising: a processing device;and a non-transitory, processor-readable storage medium, thenon-transitory, processor- readable storage medium comprising one ormore programming instructions that, when executed, cause the processingdevice to: receive a query request comprising at least one entity, thequery request configured by a user, wherein: the query request includesa user-defined threshold, the at least one entity is associated with anentity profile comprising a set of tags, a supplier tier, a suppliertier weight, a supplier category, and a supplier category weight, andthe set of tags in the entity profile are included in the query request;search a set of metadata tags stored in an index of metadatacorresponding to a set of content items related to at least oneparticular issue in a corpora of electronically stored content for atleast one term contained in the query request; identify a set ofmetadata tags that match the query request; identify at least onedocument in the corpora that is associated with the set of metadatatags; calculate a content score for each identified document; when thecontent score exceeds the user-defined threshold, surface the document;calculate a summary score for a set of documents surfaced in theprevious step based on the content scores associated with the documents,wherein the summary score averages the content scores for the set ofsurfaced documents for each of the at least one entity; provide a userinterface wherein the summary score is displayed in a grid and a user ispermitted to click through the summary score to reveal the set ofdocuments from which the summary score was derived; calculate a tierscore by averaging the summary scores for all entities assigned to agiven tier; calculate a weighted tier score for the given tier byapplying the supplier tier weight; and calculate a supplier categoryscore by selecting a maximum score associated with a supplier within agiven supplier category and applying a supplier category weight.
 2. Thesystem of claim 1, wherein: the query request comprises at least oneentity profile and at least one risk category and risk category weight;the summary score averages the content scores for the set of surfaceddocuments for each of the at least one entity profile in each of the atleast one risk category; and the user interface displays each summaryscore in a grid juxtaposing a set of suppliers against a set of riskcategories.
 3. The system of claim 2, wherein the risk categories arechosen from a list consisting of environmental issues, economic issues,societal issues, political issues, technology issues, business-specificissues, and legal issues.
 4. The system of claim 3, wherein: the userinterface is configured to expand the set of risk categories into a setof risk dimensions comprising a company perspective, an industryperspective, and a location perspective; and the summary score for thecompany perspective is based on a subset of the surfaced documentscomprising a match with at least one company name associated with theentity profile; the summary score for the industry perspective is basedon a subset of the surfaced documents comprising a match with at leastone industry tag associated with the entity profile; and the summaryscore for the location perspective is based on a subset of the surfaceddocuments comprising a match with at least one location tag associatedwith the entity profile.
 5. The system of claim 4, wherein thenon-transitory, processor-readable storage medium further comprises oneor more programming instructions that, when executed, cause theprocessing device to: provide an interactive administrative GUI to allowa user to configure at least one entity profile further comprising asupplier and a set of tags associated with the supplier, including asupplier tier, a supplier tier weight, a supplier category, and asupplier category weight; and receive and store the entity profile in acomputer-readable storage medium.
 6. The system of claim 1, wherein: thequery request further comprises at least two time periods and at leastone geographic designation; the query request further includes a subjectchosen from a set of subjects contained in the index of metadata; thesummary score counts the set of surfaced documents for each of the atleast two time periods; the user interface graphically compares thesummary score associated with each of the at least two time periods. 7.The system of claim 6, wherein the at least two time periods comprise abaseline time period and a second time period and the thresholdcomprises a minimum relevance level.
 8. The system of claim 7, whereinthe index of metadata comprises a set of geographic tags and whereineach content item in the corpora of electronically stored content isassociated with a tag corresponding to the content item's region ofpublication.
 9. The system of claim 8, wherein the user interfacegraphically displays the summary scores for each of the geographicdesignations.
 10. The system of claim 9, wherein the query requestprovides a set of weights to use as a computer machine input todetermine if a document meets or exceeds the minimum relevance level,wherein the set of weights are associated with the subject's locationand frequency in the document.
 11. A system configured to performanalytics to facilitate issue awareness, the system comprising: aprocessing device; and a non-transitory, processor-readable storagemedium, the non-transitory, processor-readable storage medium comprisingone or more programming instructions that, when executed, cause theprocessing device to: receive a query request configured by a user,wherein the query request includes a user-defined threshold, at leastone entity profile, and at least one risk category and risk categoryweight, and wherein the at least one risk category is chosen from thelist consisting of environmental issues, economic issues, societalissues, political issues, technology issues, business-specific issuesand legal issues, search a set of metadata tags stored in an index ofmetadata corresponding to a set of content items in a corpora ofelectronically stored content for at least one term contained in thequery request, identify a set of metadata tags that match the queryrequest, identify at least one document in the corpora that isassociated with the set of metadata tags, calculate a content score foreach identified document, when the content score exceeds theuser-defined threshold, surface the document, calculate a summary scorefor a set of documents surfaced in the previous step based on thecontent scores associated with the documents, wherein the summary scoreaverages the content scores for the set of surfaced documents for eachof the at least one entity profile in each of the at least one riskcategory, and provide an interactive user interface to communicate thesummary score, wherein the interactive user interface is configured to:display each summary score in a grid juxtaposing a set of suppliersagainst a set of risk categories, permit a user to click through thesummary score to reveal a set of documents from which the summary scorewas derived, and expand the set of risk categories into a set of riskdimensions comprising a company perspective, an industry perspective,and a location perspective, wherein: the summary score for the companyperspective is based on a subset of the surfaced documents comprising amatch with at least one company name associated with the entity profile,the summary score for the industry perspective is based on a subset ofthe surfaced documents comprising a match with at least one industry tagassociated with the entity profile, and the summary score for thelocation perspective is based on a subset of the surfaced documentscomprising a match with at least one location tag associated with theentity profile.
 12. The system of claim 11, wherein the non-transitory,processor-readable storage medium further comprises one or moreprogramming instructions that, when executed, cause the processingdevice to: provide an interactive administrative GUI to allow a user toconfigure at least one entity profile further comprising a supplier anda set of tags associated with the supplier, including a supplier tier, asupplier tier weight, a supplier category, and a supplier categoryweight; and receive and store the entity profile in a computer-readablestorage medium.
 13. A system configured to perform analytics tofacilitate issue awareness comprising: a processing device; and anon-transitory, processor-readable storage medium, the non-transitory,processor-readable storage medium comprising one or more programminginstructions that, when executed, cause the processing device to:receive a query request configured by a user, wherein the query requestincludes: a user-defined threshold, at least two time periods and atleast one geographic designation, wherein the at least two time periodscomprise a baseline time period and a second time period and thethreshold comprises a minimum relevance level, and a subject chosen froma set of subjects contained in an index of metadata corresponding to aset of content items in a corpora of electronically stored content,wherein the index of metadata comprises a set of geographic tags andwherein each content item in the corpora of electronically storedcontent is associated with a tag corresponding to the content item'sregion of publication, search a set of metadata tags stored in the indexfor at least one term contained in the query request; identify a set ofmetadata tags which match the query request; identify at least onedocument, in the corpora, which is associated with the set of metadatatags; calculate a content score for each document identified in theprevious step; when the content score exceeds the user-definedthreshold, surface the document; calculate a summary score for a set ofdocuments surfaced in the previous step based on the content scoresassociated with the documents, wherein the summary score counts the setof surfaced documents for each of the at least two time periods; andprovide an interactive user interface to communicate the summary score,wherein the interactive user interface is configured to: graphicallycompare the summary score associated with each of the at least two timeperiods, graphically display the summary scores for each of thegeographic designations, and permit a user to click through the summaryscore to reveal a set of documents from which the summary score wasderived.
 14. The system of claim 13, wherein the non-transitory,processor-readable storage medium further comprises one or moreprogramming instructions that, when executed, cause the processingdevice to: receive a set of weights, wherein the weights determine if adocument meets or exceeds the minimum relevance level, wherein the setof weights are associated with the subject's location and frequency inthe document.